DEVELOPING ENGAGING ENGINEERING EDUCATION RESOURCES BASED ON STUDENTS LEARNING AND EDUCATORS' TEACHING STYLES

The learning styles of engineering students have evolved over the years with the advent of technology-enhanced education. Educators have also changed their teaching styles to incorporate student-centric pedagogy and educational technology. While the COVID-19 pandemic proliferated online teaching worldwide, often forcing educators to conceptualize and convert face-to-face teaching materials to online learning modules, Ansys academic endeavours have also pivoted to support educators and digital learning. This paper highlights the Ansys Education Resources, aimed at helping undergraduate materials and simulation-based design educators teach and inspire students. These are also perfect for students and self-learners who are looking to complement classroom content. Moreover, these help professors in assigning supplemental homework or capstone projects. The resources, of which 80% are openly accessed, are categorized as lectures, case studies, micro-projects, exercises, etc., and make the educators' job easier in inspiring and engaging the students' digital learning process throughout the undergraduate curricula. Solutions to exercises and projects are restricted to educators with current Granta EduPack product licenses. In developing these resources, the content developers consider different teaching and learning styles with special emphasis on project-based learning and incorporating questions that inspire students to consider societal impact and ethical choices. Some resources are purposely prepared for discussions and debates. The impact of these resources is measured through qualitative questions-answers in user group meetings, periodical surveys, and quantitative performance (such as download numbers) of the website repositories. © 2022 SEFI 2022 - 50th Annual Conference of the European Society for Engineering Education, Proceedings. All rights reserved.

Gamification as a strategy in collaborative learning against virtual education in times of pandemic

This research focuses on the use of gamification in collaborative learning, which means, implementing game-based activities in the virtual education of students from Educación General Básica. Gamification is a strategy that seeks to increase the motivation and creativity of students in academic environments, to obtain better results in their learning. Therefore, the objective of this research was to develop author resources based on gamification tools in the collaborative learning of students at Educación General Básica. Therefore, the type of research is experimental - analytical, since it allows to analyze the experiment applied in the study population through a pretest and posttest, the ADDIE model was used for the development of resources, which consists of creating experiences in the educational process to enhance skills in students. To determine the reliability of the instrument, Cronbach's alpha statistic was used with a value of 0.842. Furthermore, the tools used were Powtoon and Kahoot which are important resources for students to improve their knowledge in an innovative and creative way. The results of this research were optimal, the students give a positive response for the development of gamification tools within the classroom since those help them to learn an interactive and dynamic way. © 2022 IEEE.

Creating an Inter-Hospital Resilient Network for Pandemic Response Based on Blockchain and Dynamic Digital Twins

Developing and using the rich data implied by dynamic digital twins and blockchain is relevant to manage both patients and medical resources (e.g., doctors/nurses, ventilators etc.) at the COVID-19 and post COVID period. This paper aims at exploring the blockchain solutions for preparing healthcare systems ready for both efficient operation daily and in pandemic through (1) information integration of patient and medical resource flow from healthcare and medical records;(2) optimizing the deployment of such resources based on hospitals, regions and local pandemic levels switching from normal to the outbreak. The main idea is to develop the concepts of the novel framework for creating an inter-hospital resilient network for pandemic response based on blockchain and dynamic digital twin, which will set up innovative ways to best care for patients, protect NHS staff, and support government scientific decisions to beat COVID-19 now and manage the crisis in the future. © 2021 IEEE.

KEEP CALM AND WORK FROM HOME: STRATEGIES USED BY DUAL-EARNER HOUSEHOLDS WITH SCHOOL-AGE CHILDREN

We examine how dual-earner couples with school-age children who worked from home during the COVID-19 lockdown adjusted to the changes it brought to their lives. To do so, we conducted a qualitative study of 28 dual-earner households who had at least one school-age child, resided in China, Iran, Malaysia, the United Kingdom, or the United States, and worked from home during the lockdown. Informed by resource-based work-home interface and family stress models, we outline the resources and demands generated by working at home as a family, as well as the strategies families employed to manage their collective work-from-home. © AoM 2021.All right reserved.

Design of a Personalized Recommendation System for Learning Resources based on Collaborative Filtering

At present, personalized recommendation system has become an indispensable technology in the fields of e-commerce, social network and news recommendation. However, the development of personalized recommendation system in the field of education and teaching is relatively slow with lack of corresponding application.In the era of Internet Plus, many colleges have adopted online learning platforms amidst the coronavirus (COVID-19) epidemic. Overwhelmed with online learning tasks, many college students are overload by learning resources and unable to keep orientation in learning. It is difficult for them to access interested learning resources accurately and efficiently. Therefore, the personalized recommendation of learning resources has become a research hotspot. This paper focuses on how to develop an effective personalized recommendation system for teaching resources and improve the accuracy of recommendation. Based on the data on learning behaviors of the online learning platform of our university, the authors explored the classic cold start problem of the popular collaborative filtering algorithm, and improved the algorithm based on the data features of the platform. Specifically, the data on learning behaviors were extracted and screened by knowledge graph. The screened data were combined with the collaborative filtering algorithm to recommend learning resources. Experimental results show that the improved algorithm effectively solved the loss of orientation in learning, and the similarity and accuracy of recommended learning resources surpassed 90%. Our algorithm can fully satisfy the personalized needs of students, and provide a reference solution to the personalized education service of intelligent online learning platforms. © 2022, North Atlantic University Union NAUN. All rights reserved.